st_profiling_clean
Written by Aunoy Poddar May 23rd, 2022
Process the puncta quantified raw data
current_file <- rstudioapi::getActiveDocumentContext()$path
output_file <- stringr::str_replace(current_file, '.Rmd', '.R')
knitr::purl(current_file, output = output_file)
file.edit(output_file)Import packages and functions
library(Seurat)
library(tictoc)
library(ggplot2)
library(patchwork)
library(pheatmap)
library(RColorBrewer)
library(tidyverse)
library(gridExtra)Load the data
data_dir = '/home/aunoy/st/arc_profiling/st_analysis/hand_annotated_data/rethresholded'
output_dir_plot = '/home/aunoy/st/arc_profiling/st_analysis/results/plots'
output_dir_tbls = '/home/aunoy/st/arc_profiling/st_analysis/results/tables'Merge both datasets and generate a metadata column that corresponds
to the cell
df = data.frame()
for (file_name in list.files(data_dir)){
print(file_name)
#if(grepl('408_TC', file_name) | grepl('408_vMS', file_name)){
# next
#}
df_to_append <- read.table(file.path(data_dir, file_name), sep = ',', header = TRUE)
while(length(ind <- which(df_to_append$Image.Name == "")) > 0){
df_to_append$Image.Name[ind] <- df_to_append$Image.Name[ind -1]
}
#df_to_append <- df_to_append %>%
# dplyr::select(-1)
if(grepl('164', file_name)){
df_to_append <- df_to_append %>%
dplyr::select(-X)
}
colnames(df_to_append) <- toupper(colnames(df_to_append))
df_to_append <- df_to_append %>%
mutate(area = strsplit(file_name, '.csv')[[1]])
if(!is_empty(df)){
df_to_append <- df_to_append %>%
dplyr::select(colnames(df))
}
df <- rbind(df, df_to_append)
}[1] "164_CC.csv"
[1] "164_MS_CC.csv"
[1] "164_MS_TC.csv"
[1] "164_TC.csv"
[1] "408_CC.csv"
[1] "408_dMS_TC.csv"
[1] "408_MS_CC.csv"
[1] "408_TC.csv"
[1] "408_vMS_TC.csv"### right now i want experiment # for the day and overwrite to be false
#
# ## Write to file
# date <- gsub('-', '', Sys.Date())
# exp_num <- '1'
# folder <- paste0(date, '_', exp_num)
#
# ## check if folder exists, if not, then proceed
# expltdir <- file.path(output_dir_plot, folder)
# extbldir <- file.path(output_dir_plot, folder)
#
#
# if(dir.exists(expltdir) || dir.exists(extbldir)){
# quit('Folder exists. Set --overwrite to TRUE to replace folder')
# }
#
# ## Create the directories
# dir.create(expltdir)
# dir.create(extbldir)Add the cellnames as rownames
rownames(df) <- c(outer(c('C'), 1:dim(df)[1], FUN=paste0))Define the different gene sets based on conversation with JY
migra = toupper(c('Dcx', 'Lrp8', 'Reln', 'Dcdc2', 'Ncam1', 'Kia0319', 'Vldlr'))
#CGE = toupper(c('Egfr', 'Vip', 'Prox1'))
#LGE = toupper(c('Tshz1', 'Gsx2', 'Emx1'))
#MGE = toupper(c('Lhx6', 'Maf1', 'Sst'))
CGE = toupper(c('Egfr', 'Prox1'))
LGE = toupper(c('Tshz1', 'Gsx2', 'Emx1'))
MGE = toupper(c('Lhx6', 'Maf1', 'Nkx2.1'))
CGE_LGE = toupper(c('Scgn', 'Couptf2', 'Sp8', 'Calb2', 'Pax6'))
GABA = toupper(c('Dlx2', 'Gad1'))
mature_IN = toupper(c('Gad1', 'Vip', 'Sst'))
progen_IN = toupper(c('Dlx2'))
Excit = toupper(c('Eomes', 'Tbr1', 'Satb2'))
ligand = toupper(c('Reln', 'Cxcl12', 'Cxcl14'))
recept = toupper(c('Lrp8', 'Cxcr7', 'Cxcr4', 'Vldlr'))Heatmaps
Rearrange the data to wide for heatmap
df_longer <- df %>%
rownames_to_column('Cell') %>%
dplyr::select(-c(area, IMAGE.NAME)) %>%
pivot_longer(!Cell, names_to = 'Gene', values_to = "Puncta2Nuc_IR")
df_wide <- df %>%
rownames_to_column('Cell')%>%
dplyr::select(-c(area, IMAGE.NAME)) %>%
pivot_longer(!Cell, names_to = 'Gene', values_to = "Puncta2Nuc_IR") %>%
pivot_wider(names_from = Gene, values_from = Puncta2Nuc_IR)some quick heatmap business
By area, not dendogram
dist_mat <- as.dist(1-cor(scaled_mat, method="spearman"))
dist_mat %>%
pheatmap(annotation_col = annotation,
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100),
cluster_rows = TRUE, cluster_cols = TRUE,
fontsize_row = 8, fontsize_col = 4, show_colnames = FALSE,
show_rownames = FALSE, cell_width = 0.1, cellheight = 0.3)#,
#filename = file.path(output_dir, 'spearmann_log.png'))df_longer %>%
filter(Gene == 'Gsx2') %>%
ggplot(aes(x=Puncta2Nuc_IR)) +
geom_histogram(color="black", fill="white")
plots = list()
save = FALSE
all_genes <- df %>%
dplyr::select(-area) %>%
colnames()
for (i in 1:length(all_genes)){
plots[[i]] <- df_longer %>%
filter(Gene == all_genes[i]) %>%
mutate(log_norm = log1_and_mult(Puncta2Nuc_IR)) %>%
ggplot(aes(x=log_norm, label = Gene)) +
geom_histogram(color="black", fill="white") +
labs(title=all_genes[i])+
theme_classic()
}
#gridExtra::grid.arrange(grobs = plots, ncol = 4, nrow = 8, lengths=2:6)
if (save){
ml <- marrangeGrob(plots, nrow=2, ncol=2)
ggsave("/home/aunoy/raw_histograms.pdf", ml)
} else{
marrangeGrob(plots, nrow=2, ncol=2)
}`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
process data in Seurat object
jyobj <- df[good_cells, ] %>%
dplyr::select(-c(area, IMAGE.NAME)) %>%
t() %>%
CreateSeuratObject()jyobj <- NormalizeData(jyobj, scale.factor = 1e5) ###Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|sum(jyobj@assays$RNA[,] < 1)[1] 14325plot highly variable genes
jyobj <- FindVariableFeatures(jyobj, selection.method = "vst")Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(jyobj), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(jyobj) + theme(axis.title.x = element_text(size = 10), axis.text.x = element_text(size = 6))
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE) + theme(axis.title.x = element_text(size = 10), axis.text.x = element_text(size = 6))When using repel, set xnudge and ynudge to 0 for optimal resultsplot1 + plot2 + theme(axis.title.y = element_text(size = 10))
#ggsave(file.path(output_dir, 'vst_plot.png'))all.genes <- rownames(jyobj)
jyobj <- ScaleData(jyobj, features = all.genes)Centering and scaling data matrix
|
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|===========================================================================================| 100%jyobj <- RunPCA(jyobj, features = VariableFeatures(object = jyobj), approx = FALSE)Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
Warning: Requested number is larger than the number of available items (32). Setting to 32.
PC_ 1
Positive: KIA0319, SATB2, DCDC2, EOMES, CALB2, TBR1, CXCL12, ASCL1, PAX6, RELN
LRP8, EGFR, EMX1, COUPTF2, NCAM1, CXCR4
Negative: GAD1, NKX2.1, MAF1, GSX2, TSHZ1, PROX1, SCGN, VIP, SST, DLX2
DCX, SP8, LHX6, CXCL14, CXCR7, VLDLR
PC_ 2
Positive: DCX, TBR1, SCGN, LRP8, EGFR, EOMES, DCDC2, COUPTF2, CALB2, NCAM1
GSX2, CXCR7, TSHZ1, KIA0319, SATB2, CXCL14
Negative: VIP, CXCR4, MAF1, GAD1, DLX2, ASCL1, RELN, SST, LHX6, PAX6
EMX1, PROX1, VLDLR, NKX2.1, CXCL12, SP8
PC_ 3
Positive: SP8, COUPTF2, NKX2.1, SCGN, PROX1, LHX6, DLX2, EMX1, TSHZ1, EOMES
TBR1, GSX2, LRP8, MAF1, CXCR4, CXCR7
Negative: NCAM1, GAD1, RELN, EGFR, SST, ASCL1, KIA0319, CXCL14, VIP, DCDC2
CALB2, CXCL12, DCX, PAX6, SATB2, VLDLR
PC_ 4
Positive: SST, LHX6, LRP8, DLX2, TBR1, PAX6, COUPTF2, EOMES, RELN, CALB2
SATB2, CXCL12, GAD1, SP8, EMX1, CXCR4
Negative: TSHZ1, NCAM1, PROX1, CXCR7, CXCL14, EGFR, VLDLR, SCGN, NKX2.1, GSX2
VIP, MAF1, DCDC2, ASCL1, DCX, KIA0319
PC_ 5
Positive: RELN, SCGN, PAX6, ASCL1, SATB2, SST, SP8, EMX1, GSX2, MAF1
CXCR7, NCAM1, VLDLR, DCX, EGFR, KIA0319
Negative: CXCR4, GAD1, LRP8, EOMES, CXCL14, LHX6, VIP, DCDC2, CXCL12, PROX1
CALB2, NKX2.1, COUPTF2, DLX2, TBR1, TSHZ1 print(jyobj[["pca"]], dims = 1:5, nfeatures = 5)PC_ 1
Positive: KIA0319, SATB2, DCDC2, EOMES, CALB2
Negative: GAD1, NKX2.1, MAF1, GSX2, TSHZ1
PC_ 2
Positive: DCX, TBR1, SCGN, LRP8, EGFR
Negative: VIP, CXCR4, MAF1, GAD1, DLX2
PC_ 3
Positive: SP8, COUPTF2, NKX2.1, SCGN, PROX1
Negative: NCAM1, GAD1, RELN, EGFR, SST
PC_ 4
Positive: SST, LHX6, LRP8, DLX2, TBR1
Negative: TSHZ1, NCAM1, PROX1, CXCR7, CXCL14
PC_ 5
Positive: RELN, SCGN, PAX6, ASCL1, SATB2
Negative: CXCR4, GAD1, LRP8, EOMES, CXCL14 VizDimLoadings(jyobj, dims = 1:2, reduction = "pca")
jyobj$area <- df[good_cells,]$area
DimPlot(jyobj, reduction = "pca", group.by = 'area')
ElbowPlot(jyobj)
PC30,resolution 0.8
jyobj <- FindNeighbors(jyobj, dims = 1:30)Computing nearest neighbor graph
Computing SNNjyobj <- FindClusters(jyobj, resolution = 1.4)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5912
Number of communities: 10
Elapsed time: 0 secondsjyobj <- RunUMAP(jyobj, dims = 1:30)17:39:53 UMAP embedding parameters a = 0.9922 b = 1.112
17:39:53 Read 1285 rows and found 30 numeric columns
17:39:53 Using Annoy for neighbor search, n_neighbors = 30
17:39:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:39:53 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443eeff1361
17:39:53 Searching Annoy index using 1 thread, search_k = 3000
17:39:54 Annoy recall = 100%
17:39:54 Commencing smooth kNN distance calibration using 1 thread
17:39:55 Initializing from normalized Laplacian + noise
17:39:55 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:39:57 Optimization finishedDimPlot(jyobj, reduction = "umap", group.by = 'seurat_clusters')
Compute scores for groups of genes
jyobj.markers <- FindAllMarkers(jyobj, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 0
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
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group_by(cluster) %>%
slice_max(n = 32, order_by = avg_log2FC)VlnPlot(jyobj,c("SATB2","GAD1"))
DCX GAD1 COUPTF2 SP8 TSHZ1 NKX2.1
# DCX
# GAD1breakpoints = c(0.2, 0.25, 0.45, 0.5, 0.55, 0.6, 0.65,
0.7, 0.75, 0.8, 0.9, 0.95, 1, 1.05, 1.4, 1.45, 1.6, 1.65, 1.85, 1.9)
plots = list()
jyobj <- FindNeighbors(jyobj, dims = 1:30)Computing nearest neighbor graph
Computing SNNi = 1
for (breakpoint in breakpoints){
jyobj <- FindClusters(jyobj, resolution = breakpoint)
jyobj <- RunUMAP(jyobj, dims = 1:30)
jyobj.markers <- FindAllMarkers(jyobj, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
labels = jyobj.markers %>%
group_by(cluster) %>%
slice_max(n = 1, order_by = avg_log2FC)
new.cluster.ids <- labels$gene
names(new.cluster.ids) <- levels(jyobj)
jyobj <- RenameIdents(jyobj, new.cluster.ids)
plots[[i]] = DimPlot(jyobj, reduction = "umap", pt.size = 1, label = TRUE) + NoAxes() + NoLegend()
i = i + 1
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8236
Number of communities: 2
Elapsed time: 0 seconds17:41:18 UMAP embedding parameters a = 0.9922 b = 1.112
17:41:18 Read 1285 rows and found 30 numeric columns
17:41:18 Using Annoy for neighbor search, n_neighbors = 30
17:41:18 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:18 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e2824dc0a
17:41:18 Searching Annoy index using 1 thread, search_k = 3000
17:41:18 Annoy recall = 100%
17:41:19 Commencing smooth kNN distance calibration using 1 thread
17:41:19 Initializing from normalized Laplacian + noise
17:41:19 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:21 Optimization finished
Calculating cluster 0
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8044
Number of communities: 3
Elapsed time: 0 seconds17:41:22 UMAP embedding parameters a = 0.9922 b = 1.112
17:41:22 Read 1285 rows and found 30 numeric columns
17:41:22 Using Annoy for neighbor search, n_neighbors = 30
17:41:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:22 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e78b46f58
17:41:22 Searching Annoy index using 1 thread, search_k = 3000
17:41:22 Annoy recall = 100%
17:41:23 Commencing smooth kNN distance calibration using 1 thread
17:41:24 Initializing from normalized Laplacian + noise
17:41:24 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:25 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++ | 6 % ~00s
|++++++ | 12% ~00s
|+++++++++ | 18% ~00s
|++++++++++++ | 24% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 35% ~00s
|+++++++++++++++++++++ | 41% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7458
Number of communities: 4
Elapsed time: 0 seconds17:41:26 UMAP embedding parameters a = 0.9922 b = 1.112
17:41:26 Read 1285 rows and found 30 numeric columns
17:41:26 Using Annoy for neighbor search, n_neighbors = 30
17:41:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:26 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e796f41c4
17:41:26 Searching Annoy index using 1 thread, search_k = 3000
17:41:27 Annoy recall = 100%
17:41:27 Commencing smooth kNN distance calibration using 1 thread
17:41:28 Initializing from normalized Laplacian + noise
17:41:28 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:30 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++ | 6 % ~00s
|+++++++ | 12% ~00s
|++++++++++ | 19% ~00s
|+++++++++++++ | 25% ~00s
|++++++++++++++++ | 31% ~00s
|+++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7332
Number of communities: 6
Elapsed time: 0 seconds17:41:30 UMAP embedding parameters a = 0.9922 b = 1.112
17:41:30 Read 1285 rows and found 30 numeric columns
17:41:30 Using Annoy for neighbor search, n_neighbors = 30
17:41:30 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:30 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e4a8793d3
17:41:30 Searching Annoy index using 1 thread, search_k = 3000
17:41:31 Annoy recall = 100%
17:41:31 Commencing smooth kNN distance calibration using 1 thread
17:41:32 Initializing from normalized Laplacian + noise
17:41:32 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:34 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7221
Number of communities: 7
Elapsed time: 0 seconds17:41:35 UMAP embedding parameters a = 0.9922 b = 1.112
17:41:35 Read 1285 rows and found 30 numeric columns
17:41:35 Using Annoy for neighbor search, n_neighbors = 30
17:41:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:35 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e173728a8
17:41:35 Searching Annoy index using 1 thread, search_k = 3000
17:41:35 Annoy recall = 100%
17:41:36 Commencing smooth kNN distance calibration using 1 thread
17:41:36 Initializing from normalized Laplacian + noise
17:41:36 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:38 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7115
Number of communities: 7
Elapsed time: 0 seconds17:41:39 UMAP embedding parameters a = 0.9922 b = 1.112
17:41:39 Read 1285 rows and found 30 numeric columns
17:41:39 Using Annoy for neighbor search, n_neighbors = 30
17:41:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:39 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e5288b983
17:41:39 Searching Annoy index using 1 thread, search_k = 3000
17:41:40 Annoy recall = 100%
17:41:40 Commencing smooth kNN distance calibration using 1 thread
17:41:41 Initializing from normalized Laplacian + noise
17:41:41 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:42 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7007
Number of communities: 7
Elapsed time: 0 seconds17:41:43 UMAP embedding parameters a = 0.9922 b = 1.112
17:41:43 Read 1285 rows and found 30 numeric columns
17:41:43 Using Annoy for neighbor search, n_neighbors = 30
17:41:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:44 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e5d6af73e
17:41:44 Searching Annoy index using 1 thread, search_k = 3000
17:41:44 Annoy recall = 100%
17:41:44 Commencing smooth kNN distance calibration using 1 thread
17:41:45 Initializing from normalized Laplacian + noise
17:41:45 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:47 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6898
Number of communities: 7
Elapsed time: 0 seconds17:41:48 UMAP embedding parameters a = 0.9922 b = 1.112
17:41:48 Read 1285 rows and found 30 numeric columns
17:41:48 Using Annoy for neighbor search, n_neighbors = 30
17:41:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:48 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443ec8c941a
17:41:48 Searching Annoy index using 1 thread, search_k = 3000
17:41:48 Annoy recall = 100%
17:41:49 Commencing smooth kNN distance calibration using 1 thread
17:41:49 Initializing from normalized Laplacian + noise
17:41:49 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:51 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6805
Number of communities: 7
Elapsed time: 0 seconds17:41:52 UMAP embedding parameters a = 0.9922 b = 1.112
17:41:52 Read 1285 rows and found 30 numeric columns
17:41:52 Using Annoy for neighbor search, n_neighbors = 30
17:41:52 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:52 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e1c2bc809
17:41:52 Searching Annoy index using 1 thread, search_k = 3000
17:41:53 Annoy recall = 100%
17:41:53 Commencing smooth kNN distance calibration using 1 thread
17:41:54 Initializing from normalized Laplacian + noise
17:41:54 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:56 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6717
Number of communities: 7
Elapsed time: 0 seconds17:41:57 UMAP embedding parameters a = 0.9922 b = 1.112
17:41:57 Read 1285 rows and found 30 numeric columns
17:41:57 Using Annoy for neighbor search, n_neighbors = 30
17:41:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:41:57 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e6b3149f5
17:41:57 Searching Annoy index using 1 thread, search_k = 3000
17:41:57 Annoy recall = 100%
17:41:58 Commencing smooth kNN distance calibration using 1 thread
17:41:59 Initializing from normalized Laplacian + noise
17:41:59 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:01 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6549
Number of communities: 8
Elapsed time: 0 seconds17:42:01 UMAP embedding parameters a = 0.9922 b = 1.112
17:42:01 Read 1285 rows and found 30 numeric columns
17:42:01 Using Annoy for neighbor search, n_neighbors = 30
17:42:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:01 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e7e2a028
17:42:01 Searching Annoy index using 1 thread, search_k = 3000
17:42:02 Annoy recall = 100%
17:42:02 Commencing smooth kNN distance calibration using 1 thread
17:42:03 Initializing from normalized Laplacian + noise
17:42:03 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:05 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6474
Number of communities: 9
Elapsed time: 0 seconds17:42:06 UMAP embedding parameters a = 0.9922 b = 1.112
17:42:06 Read 1285 rows and found 30 numeric columns
17:42:06 Using Annoy for neighbor search, n_neighbors = 30
17:42:06 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:06 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e155af36a
17:42:06 Searching Annoy index using 1 thread, search_k = 3000
17:42:06 Annoy recall = 100%
17:42:06 Commencing smooth kNN distance calibration using 1 thread
17:42:07 Initializing from normalized Laplacian + noise
17:42:07 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:09 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6407
Number of communities: 9
Elapsed time: 0 seconds17:42:10 UMAP embedding parameters a = 0.9922 b = 1.112
17:42:10 Read 1285 rows and found 30 numeric columns
17:42:10 Using Annoy for neighbor search, n_neighbors = 30
17:42:10 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:10 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e324ffeb8
17:42:10 Searching Annoy index using 1 thread, search_k = 3000
17:42:11 Annoy recall = 100%
17:42:11 Commencing smooth kNN distance calibration using 1 thread
17:42:12 Initializing from normalized Laplacian + noise
17:42:12 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:14 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6328
Number of communities: 9
Elapsed time: 0 seconds17:42:15 UMAP embedding parameters a = 0.9922 b = 1.112
17:42:15 Read 1285 rows and found 30 numeric columns
17:42:15 Using Annoy for neighbor search, n_neighbors = 30
17:42:15 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:15 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e345cb3b8
17:42:15 Searching Annoy index using 1 thread, search_k = 3000
17:42:15 Annoy recall = 100%
17:42:16 Commencing smooth kNN distance calibration using 1 thread
17:42:16 Initializing from normalized Laplacian + noise
17:42:16 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:19 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5912
Number of communities: 10
Elapsed time: 0 seconds17:42:20 UMAP embedding parameters a = 0.9922 b = 1.112
17:42:20 Read 1285 rows and found 30 numeric columns
17:42:20 Using Annoy for neighbor search, n_neighbors = 30
17:42:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:20 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e9b008a7
17:42:20 Searching Annoy index using 1 thread, search_k = 3000
17:42:20 Annoy recall = 100%
17:42:20 Commencing smooth kNN distance calibration using 1 thread
17:42:21 Initializing from normalized Laplacian + noise
17:42:21 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:23 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5861
Number of communities: 12
Elapsed time: 0 seconds17:42:24 UMAP embedding parameters a = 0.9922 b = 1.112
17:42:24 Read 1285 rows and found 30 numeric columns
17:42:24 Using Annoy for neighbor search, n_neighbors = 30
17:42:24 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:24 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e26b12448
17:42:24 Searching Annoy index using 1 thread, search_k = 3000
17:42:25 Annoy recall = 100%
17:42:25 Commencing smooth kNN distance calibration using 1 thread
17:42:26 Initializing from normalized Laplacian + noise
17:42:26 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:28 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|++++ | 8 % ~00s
|++++++++ | 15% ~00s
|++++++++++++ | 23% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5719
Number of communities: 13
Elapsed time: 0 seconds17:42:29 UMAP embedding parameters a = 0.9922 b = 1.112
17:42:29 Read 1285 rows and found 30 numeric columns
17:42:29 Using Annoy for neighbor search, n_neighbors = 30
17:42:29 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:29 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e505fa38c
17:42:29 Searching Annoy index using 1 thread, search_k = 3000
17:42:29 Annoy recall = 100%
17:42:30 Commencing smooth kNN distance calibration using 1 thread
17:42:31 Initializing from normalized Laplacian + noise
17:42:31 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:33 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|++++ | 7 % ~00s
|+++++++ | 13% ~00s
|++++++++++ | 20% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5675
Number of communities: 13
Elapsed time: 0 seconds17:42:34 UMAP embedding parameters a = 0.9922 b = 1.112
17:42:34 Read 1285 rows and found 30 numeric columns
17:42:34 Using Annoy for neighbor search, n_neighbors = 30
17:42:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:34 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e3dd7e4a7
17:42:34 Searching Annoy index using 1 thread, search_k = 3000
17:42:34 Annoy recall = 100%
17:42:35 Commencing smooth kNN distance calibration using 1 thread
17:42:35 Initializing from normalized Laplacian + noise
17:42:36 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:38 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|++++ | 7 % ~00s
|+++++++ | 13% ~00s
|++++++++++ | 20% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5503
Number of communities: 13
Elapsed time: 0 seconds17:42:39 UMAP embedding parameters a = 0.9922 b = 1.112
17:42:39 Read 1285 rows and found 30 numeric columns
17:42:39 Using Annoy for neighbor search, n_neighbors = 30
17:42:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:39 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e1a6cf1c2
17:42:39 Searching Annoy index using 1 thread, search_k = 3000
17:42:39 Annoy recall = 100%
17:42:40 Commencing smooth kNN distance calibration using 1 thread
17:42:40 Initializing from normalized Laplacian + noise
17:42:40 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:42 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|++++++++ | 14% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|++++ | 7 % ~00s
|+++++++ | 13% ~00s
|++++++++++ | 20% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
|++++ | 7 % ~00s
|++++++++ | 14% ~00s
|+++++++++++ | 21% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++++ | 36% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1285
Number of edges: 45891
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.5457
Number of communities: 14
Elapsed time: 0 seconds17:42:43 UMAP embedding parameters a = 0.9922 b = 1.112
17:42:43 Read 1285 rows and found 30 numeric columns
17:42:43 Using Annoy for neighbor search, n_neighbors = 30
17:42:43 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:44 Writing NN index file to temp file /tmp/RtmpjFYfNO/file18443e6b9375c8
17:42:44 Searching Annoy index using 1 thread, search_k = 3000
17:42:44 Annoy recall = 100%
17:42:44 Commencing smooth kNN distance calibration using 1 thread
17:42:45 Initializing from normalized Laplacian + noise
17:42:45 Commencing optimization for 500 epochs, with 48744 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:42:47 Optimization finished
Calculating cluster 0
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 1
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 4
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 5
| | 0 % ~calculating
|+++++ | 9 % ~00s
|++++++++++ | 18% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 6
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 7
| | 0 % ~calculating
|+++++ | 8 % ~00s
|+++++++++ | 17% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++ | 42% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 8
| | 0 % ~calculating
|+++++ | 10% ~00s
|++++++++++ | 20% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 9
| | 0 % ~calculating
|++++++ | 11% ~00s
|++++++++++++ | 22% ~00s
|+++++++++++++++++ | 33% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 10
| | 0 % ~calculating
|+++++++ | 12% ~00s
|+++++++++++++ | 25% ~00s
|+++++++++++++++++++ | 38% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 11
| | 0 % ~calculating
|++++ | 7 % ~00s
|+++++++ | 13% ~00s
|++++++++++ | 20% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++++ | 40% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 12
| | 0 % ~calculating
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---
title: "st_profiling_clean"
output: html_notebook
---

Written by Aunoy Poddar
May 23rd, 2022

# Process the puncta quantified raw data
```{r eval=FALSE}
current_file <- rstudioapi::getActiveDocumentContext()$path
output_file <- stringr::str_replace(current_file, '.Rmd', '.R')
knitr::purl(current_file, output = output_file)
file.edit(output_file)
```

## Import packages and functions
```{r}
library(Seurat)
library(tictoc)
library(ggplot2)
library(patchwork)
library(pheatmap)
library(RColorBrewer)
library(tidyverse)
library(gridExtra)
```

## Load the data
```{r}
data_dir = '/home/aunoy/st/arc_profiling/st_analysis/hand_annotated_data/rethresholded'
output_dir_plot = '/home/aunoy/st/arc_profiling/st_analysis/results/plots'
output_dir_tbls = '/home/aunoy/st/arc_profiling/st_analysis/results/tables'
```

### Merge both datasets and generate a metadata column that corresponds
### to the cell #
```{r}
df = data.frame()
for (file_name in list.files(data_dir)){
  print(file_name)
  #if(grepl('408_TC', file_name) | grepl('408_vMS', file_name)){
  #  next
  #}
  df_to_append <- read.table(file.path(data_dir, file_name), sep = ',', header = TRUE)
  while(length(ind <- which(df_to_append$Image.Name == "")) > 0){
    df_to_append$Image.Name[ind] <- df_to_append$Image.Name[ind -1]
  }
  #df_to_append <- df_to_append %>%
  #  dplyr::select(-1)
  if(grepl('164', file_name)){
    df_to_append <- df_to_append %>%
      dplyr::select(-X)
  }
  colnames(df_to_append) <- toupper(colnames(df_to_append))
  df_to_append <- df_to_append %>%
    mutate(area = strsplit(file_name, '.csv')[[1]])
  if(!is_empty(df)){
    df_to_append <- df_to_append %>%
          dplyr::select(colnames(df))
  }
  df <- rbind(df, df_to_append)
}
```

```{r}
### right now i want experiment # for the day and overwrite to be false
# 
# ## Write to file
# date <- gsub('-', '', Sys.Date())
# exp_num <- '1'
# folder <- paste0(date, '_', exp_num)
# 
# ## check if folder exists, if not, then proceed
# expltdir <- file.path(output_dir_plot, folder)
# extbldir <- file.path(output_dir_plot, folder)
# 
# 
# if(dir.exists(expltdir) || dir.exists(extbldir)){
#   quit('Folder exists. Set --overwrite to TRUE to replace folder')
# }
# 
# ## Create the directories
# dir.create(expltdir)
# dir.create(extbldir)
```


### Add the cellnames as rownames
```{r}
rownames(df) <- c(outer(c('C'), 1:dim(df)[1], FUN=paste0))
```

## Define the different gene sets based on conversation with JY
```{r}
migra = toupper(c('Dcx', 'Lrp8', 'Reln', 'Dcdc2', 'Ncam1', 'Kia0319', 'Vldlr'))
#CGE = toupper(c('Egfr', 'Vip', 'Prox1'))
#LGE = toupper(c('Tshz1', 'Gsx2', 'Emx1'))
#MGE = toupper(c('Lhx6', 'Maf1', 'Sst'))
CGE = toupper(c('Egfr', 'Prox1'))
LGE = toupper(c('Tshz1', 'Gsx2', 'Emx1'))
MGE = toupper(c('Lhx6', 'Maf1', 'Nkx2.1'))
CGE_LGE = toupper(c('Scgn', 'Couptf2', 'Sp8', 'Calb2', 'Pax6'))
GABA = toupper(c('Dlx2', 'Gad1'))
mature_IN = toupper(c('Gad1', 'Vip', 'Sst'))
progen_IN = toupper(c('Dlx2'))
Excit = toupper(c('Eomes', 'Tbr1', 'Satb2'))
ligand = toupper(c('Reln', 'Cxcl12', 'Cxcl14'))
recept = toupper(c('Lrp8', 'Cxcr7', 'Cxcr4', 'Vldlr'))
```

## Heatmaps

### Rearrange the data to wide for heatmap
```{r}
df_longer <- df %>%
  rownames_to_column('Cell') %>%
  dplyr::select(-c(area, IMAGE.NAME)) %>%
  pivot_longer(!Cell, names_to = 'Gene', values_to = "Puncta2Nuc_IR")

df_wide <- df %>%
  rownames_to_column('Cell')%>%
  dplyr::select(-c(area, IMAGE.NAME)) %>%
  pivot_longer(!Cell, names_to = 'Gene', values_to = "Puncta2Nuc_IR") %>%
  pivot_wider(names_from = Gene, values_from = Puncta2Nuc_IR)
```


## some quick heatmap business

```{r}
### log(x+1) function
log1 <- function(x) {
   return(log(x+1))
}

### Scale and then log+1
log1_and_mult <- function(x) {
   return(log1(x*1e4))
}

### Get the numeric values only
df_num <- df_wide %>%
  column_to_rownames('Cell')

### Normalize to cell total
### Log + Multiply
### Center and scale
scaled_mat <- df_num %>%
  sweep(1, rowSums(df_num), '/') %>%
  log1_and_mult() %>%
  apply(1, scale)



### Apply separate clustering, need to look into why 
hr <- hclust(as.dist(1-cor(t(scaled_mat), method="pearson")), method = "complete")
hc <- hclust(as.dist(1-cor(scaled_mat[,good_cells], method="spearman")), method="complete")

annotation <- data.frame(area = factor(df[good_cells,]$area))
rownames(annotation) <- rownames(df[good_cells, ]) # check out the row names of annotation

rownames(scaled_mat) <- colnames(df_num)
scaled_mat[,good_cells] %>%
  pheatmap(annotation_col = annotation, 
           color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100),
  #breaks = seq(from= -5, to = 5, by = 11/100.), 
  cluster_rows = hr, cluster_cols = hc, 
  #cluster_rows = TRUE, cluster_cols = TRUE, 
  #clustering_distance_rows = "correlation", 
  #clustering_distance_cols = "correlation",
  fontsize_row = 8, fontsize_col = 4, show_colnames = FALSE,
           show_rownames = TRUE, cell_width = 6, cellheight = 8)#,
          #filename = file.path(output_dir, 'log.png'))
```
### By area, not dendogram
```{r}
rownames(scaled_mat) <- colnames(df_num)
annotation <- data.frame(clust = Idents(jyobj))
scaled_mat %>%
  pheatmap(annotation_col = annotation, 
           color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100),
  #breaks = seq(from= -5, to = 5, by = 11/100.), 
  cluster_rows = hr, cluster_cols = TRUE, 
  #cluster_rows = TRUE, cluster_cols = TRUE, 
  #clustering_distance_rows = "correlation", 
  #clustering_distance_cols = "correlation",
  fontsize_row = 8, fontsize_col = 4, show_colnames = FALSE,
           show_rownames = TRUE, cell_width = 6, cellheight = 8)#,
          # filename = '/home/aunoy/pheatmap_logz_norm.png')
```


```{r}
dist_mat <- as.dist(1-cor(scaled_mat, method="spearman"))
dist_mat %>%
pheatmap(annotation_col = annotation,
         color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100),
        cluster_rows = TRUE, cluster_cols = TRUE,
fontsize_row = 8, fontsize_col = 4, show_colnames = FALSE,
         show_rownames = FALSE, cell_width = 0.1, cellheight = 0.3)#,
    #filename = file.path(output_dir, 'spearmann_log.png'))
```


```{r}
df_longer %>%
  filter(Gene == 'Gsx2') %>%
  ggplot(aes(x=Puncta2Nuc_IR)) + 
  geom_histogram(color="black", fill="white")
```
```{r}
plots = list()
save = FALSE
all_genes <- df %>% 
  dplyr::select(-area) %>%
  colnames()

for (i in 1:length(all_genes)){
  plots[[i]] <- df_longer %>%
  filter(Gene == all_genes[i]) %>%
  mutate(log_norm = log1_and_mult(Puncta2Nuc_IR)) %>%
  ggplot(aes(x=log_norm, label = Gene)) + 
  geom_histogram(color="black", fill="white") + 
  labs(title=all_genes[i])+
  theme_classic()
}

#gridExtra::grid.arrange(grobs = plots, ncol = 4, nrow = 8, lengths=2:6)
if (save){
  ml <- marrangeGrob(plots, nrow=2, ncol=2)
  ggsave("/home/aunoy/raw_histograms.pdf", ml)
} else{
  marrangeGrob(plots, nrow=2, ncol=2)
}
```

### process data in Seurat object

```{r}
jyobj <- df[good_cells, ] %>%
  dplyr::select(-c(area, IMAGE.NAME)) %>%
  t() %>%
  CreateSeuratObject()
```


```{r}
jyobj <- NormalizeData(jyobj, scale.factor = 1e5) ###
sum(jyobj@assays$RNA[,] < 1)

```
### plot highly variable genes
```{r}
jyobj <- FindVariableFeatures(jyobj, selection.method = "vst")

# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(jyobj), 10)

# plot variable features with and without labels
plot1 <- VariableFeaturePlot(jyobj) + theme(axis.title.x = element_text(size = 10), axis.text.x = element_text(size = 6))
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE) + theme(axis.title.x = element_text(size = 10), axis.text.x = element_text(size = 6))
plot1 + plot2 + theme(axis.title.y = element_text(size = 10))
#ggsave(file.path(output_dir, 'vst_plot.png'))
```

```{r}
all.genes <- rownames(jyobj)
jyobj <- ScaleData(jyobj, features = all.genes)
```

```{r}
jyobj <- RunPCA(jyobj, features = VariableFeatures(object = jyobj), approx = FALSE)
print(jyobj[["pca"]], dims = 1:5, nfeatures = 5)
```
```{r}
VizDimLoadings(jyobj, dims = 1:2, reduction = "pca")
```

```{r}
jyobj$area <- df[good_cells,]$area
DimPlot(jyobj, reduction = "pca", group.by = 'area')

```

```{r}
ElbowPlot(jyobj)
```

# PC30,resolution 0.8

```{r}
jyobj <- FindNeighbors(jyobj, dims = 1:30)
jyobj <- FindClusters(jyobj, resolution = 1.4)
```


```{r}
jyobj <- RunUMAP(jyobj, dims = 1:30)

DimPlot(jyobj, reduction = "umap", group.by = 'seurat_clusters')
```

## Compute scores for groups of genes

```{r}
jyobj.markers <- FindAllMarkers(jyobj, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
jyobj.markers %>%
   group_by(cluster) %>%
   slice_max(n = 32, order_by = avg_log2FC)
```
```{r}
VlnPlot(jyobj,c("SATB2","GAD1"))
```

DCX
GAD1
COUPTF2
SP8
TSHZ1
NKX2.1
```{r}
# DCX
# GAD1
```

```{r}
breakpoints = c(0.2, 0.25, 0.45, 0.5, 0.55, 0.6, 0.65,
                0.7, 0.75, 0.8, 0.9, 0.95, 1, 1.05, 1.4, 1.45, 1.6, 1.65, 1.85, 1.9)

plots = list()
jyobj <- FindNeighbors(jyobj, dims = 1:30)
i = 1
for (breakpoint in breakpoints){
  jyobj <- FindClusters(jyobj, resolution = breakpoint)
  jyobj <- RunUMAP(jyobj, dims = 1:30)
  jyobj.markers <- FindAllMarkers(jyobj, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
  labels = jyobj.markers %>%
   group_by(cluster) %>%
   slice_max(n = 1, order_by = avg_log2FC)
  new.cluster.ids <- labels$gene
  names(new.cluster.ids) <- levels(jyobj)
  jyobj <- RenameIdents(jyobj, new.cluster.ids)
  plots[[i]] = DimPlot(jyobj, reduction = "umap", pt.size = 1, label = TRUE) + NoAxes() + NoLegend()
  i = i + 1
}

marrangeGrob(plots, nrow=2, ncol=2)
```


